Health care economic evaluations assess the costs and consequences of competing interventions, programmes or services. Such assessments use a decision model, with parameters informed by available evidence. Evidence, however, is rarely derived from a single source, in which case researchers are expected to combine information on multiple sources. This thesis contributes to the methodological debate on the use of evidence, particularly, the use of individual level data (IPD), for cost effectiveness analysis. This thesis defines a taxonomy which summarises the methodological and analytical issues in the use and synthesis of evidence for cost effectiveness modelling. For alternative parameter types (e.g. relative effectiveness, costs) the taxonomy offers guidance on appropriate synthesis methodologies to use and identifies areas where further methodological contributions are needed. The thesis also explores methods of synthesis of IPD and develops novel frameworks which allow both IPD and AD to be jointly modelled, specifically in estimating relative effectiveness. The use of IPD from studies is found desirable, particularly when the estimation of subgroup effects is of interest. An applied decision model of the cost effectiveness of smoke alarm equipment in households with pre-school children is developed within this thesis. This application offers a means to evaluate the impact of using IPD on the cost effectiveness outcomes, compared to the use of AD. The thesis examines the advantages of having access to IPD when quantifying decision uncertainty. Additionally, it discusses the use of IPD in estimating the value of further research. Specifically, a framework is used which allows considering population subgroups. It is argued that the use of IPD allows a more suitable characterisation of decision uncertainty, appropriately allowing for subgroup value of information analysis.

This focused ethnography considers children’s understandings and experiences of the body, and more specifically asks the question, ‘how do children come to know about the body?’. The study draws heavily upon the methodological ideas of the social studies of childhood, particularly the work of James (1993, 2013), to explore this question with nine and ten year old participants in two primary schools located in a northern English city. Findings highlight the complex interplay between structure and agency in understanding how children come to know about the body. Furthermore, children’s social and cultural locatedness, it is shown, shapes the ways in which they come to know about the body. Yet, the work of individual children in making sense of the body according to their particular experience is also highlighted. Indeed, it is through children’s experiential knowledge of the body that they come to challenge adult knowledge of, and control over, their bodies in school. Wider implications of the findings of this project include a more in-depth understanding of how children learn, which challenges the traditional notion that knowledge is passed down in a linear succession from adults to children. This, it is argued, has particular consequences in relation to understandings of children’s engagement with public health policy and formal learning about the body in school.

Health economic models are representations of judgements about the relationships between the model's input parameters and the costs and health effects that the model aims to predict. We recognise that we can rarely define with certainty a 'true' model for a particular decision problem. Building an 'incorrect' model will result in an uncertain prediction error, which we denote 'structural uncertainty'. The absence of observations on the total costs and health effects under each decision option limits the use of data driven approaches to managing structural uncertainty, such as model averaging. We therefore propose a discrepancy based approach in which we make judgements about structural error at the sub-function level within the model and introduce a series of terms to 'correct' the errors. This is deemed to be easier than making meaningful statements about the error at the level of the model output. The specification of discrepancy terms within the model also allows us to use sensitivity analysis methods to determine the relative importance of the different structural uncertainties in driving output and decision uncertainty. Following the computation of either the main effect index or the partial expected value of perfect information for each discrepancy term, we can review the structure of those parts of the model where structural uncertainty is an important source of model output or decision uncertainty. We interpret the overall expected value of perfect information for all the discrepancy terms as an upper bound on the expected value of model improvement (EVMI). We illustrate the sub-function discrepancy method in two case studies: a simple decision tree, and a more complex Markov model. Finally, we propose an efficient method for computing the main effect index and the partial expected value of perfect information when inputs and/or discrepancies are correlated.

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